Abstract

Processing high-dimensional data is a problem in data analyzation. Due to its high-dimensional attributes and little samples, it is easy to encounter overfitting in processing high-dimensional data. Multitask learning, a weak learning, can boost prediction accuracy in all kinds of learning machines. Multitask learning doesn't need complex operations, but performs well. The characteristic of multitask learning is very fit in high-dimensional data. In this paper, we propose a novel neural network-multitask self- organizing map. In multitask self-organizing map, we replace Euclidean distance between two nodes with their hellinger distance. Through experimenting on real data, multitask self-organizing map gets satisfactory results. Experiments prove multitask self-organizing map can perform well in high- dimensional data.

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